A deep learning and novelty detection framework for rapid phenotyping in high-content screening

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classi...

Full description

Saved in:
Bibliographic Details
Published in:Molecular biology of the cell 2017-11, Vol.28 (23), p.3428-3436
Main Authors: Sommer, Christoph, Hoefler, Rudolf, Samwer, Matthias, Gerlich, Daniel W
Format: Article
Language:English
Subjects:
Algorithms
Animals
Biological Variation, Population - genetics
High-Throughput Screening Assays - methods
High-Throughput Screening Assays - statistics & numerical data
Humans
Machine Learning
Numerical Analysis, Computer-Assisted
Phenotype
Software
Statistics as Topic - methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with , a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.
ISSN:1059-1524
1939-4586
DOI:10.1091/mbc.e17-05-0333